ArticlePlant methods2025
Coconut tree modeling based on abiotic factors and modified cosserat rod theory.
Article in Plant methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The biomechanics of growing trees, particularly coconut trees, are intricate due to various abiotic factors such as sunlight, wind, gravitropism, and cultivation practices. Existing structural growth models fail to capture the unique characteristics of coconut trees, which lack branches and have large crown leaves. This research introduces a novel coconut tree modeling approach, integrating abiotic factors and modified Cosserat rod theory. Factors like sunlight availability, wind speed, cultivation practices, and gravitropism influence coconut tree growth rates. The model encompasses both primary and secondary growth processes. Primary growth is influenced by gravitropism, sunlight availability, and wind effects, while secondary growth is determined by variations in trunk diameter. Additionally, the model incorporates the diameter at breast height to accommodate cultivation practice variations. Comparisons between the proposed model, classical rod theory, and biomechanics growth models reveal that the proposed model aligns more closely with real-time data on spatial and temporal growth characteristics. This research marks the first attempt to model coconut tree growth considering abiotic factors comprehensively. In summary, this study presents a pioneering coconut tree growth model that integrates abiotic factors and modified Cosserat rod theory. By considering unique features of coconut trees and environmental influences, the model offers more accurate predictions compared to existing approaches, enhancing our understanding of coconut tree biomechanics and growth patterns. Coconut tree modeling has diverse applications in precision agriculture, automated harvesting, tree health monitoring, climate change analysis, urban planning, and the biomass industry, helping optimize yield, resource management, and sustainability. It also plays a crucial role in genetic research, disaster preparedness, and risk assessment, enabling advancements in robotics, environmental conservation, and industrial applications for improved productivity and resilience.
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